Customizable pipeline for integrating data

ABSTRACT

Systems, methods, and non-transitory computer readable media are provided for customizing pipelines for integrating data. A file to be ingested into a data analysis platform may be determined. The file type of the file may be detected. The file may be transformed based on the file type. The transformation may include applying a set of operations to the file. The set of operations may correspond to a pipeline of operations associated with the file type.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/035,250, filed Jul. 13, 2018, which claims thebenefit under 35 U.S.C. § 119(e) of U.S. Provisional Application Ser.No. 62/545,215, filed Aug. 14, 2017, the content of which isincorporated by reference in its entirety into the present disclosure.

FIELD OF THE INVENTION

This disclosure relates to customizable pipeline for integrating data.

BACKGROUND

Under conventional approaches, various types of information (e.g.,files) may be provided to a data analysis platform for performing myriadoperations (e.g., viewing, processing, modifying, etc.). In general, thedata analysis system typically needs to be able to process files thatvary in type and formatting to ensure that data included in these filescan accurately be integrated.

SUMMARY

Various embodiments of the present disclosure may include systems,methods, and non-transitory computer readable media configured todetermine a file to be ingested into a data analysis platform. The filetype of the file may be detected. The file may be transformed based onthe file type. The transformation may include applying a set ofoperations to the file. The set of operations may correspond to apipeline of operations associated with the file type. The pipeline ofoperations may be defined by a template specification. The pipeline ofoperations may be customizable. The transformed file may be stored basedon completion of all operations within the pipeline of operations.

In some embodiments, the pipeline of operations may include one or moreserial operations, parallel operations, and one or more join operations.The join operation(s) may merge two or more results of the paralleloperations. For example, data included in a single or multiple files maybe operated on in parallel, and the results of the parallel operationmay be joined together for use by a data analysis platform.

In some embodiments, the pipeline of operations may include anormalization operation. For example, data included in multiple filesmay be of different type (e.g., entered using separate scales) and thedata may be normalized (e.g., into a common scale) so that the data maybe joined/used together by a data analysis platform.

In some embodiments, the pipeline of operations may include an enrichingoperation. For example, data included in a file may be supplemented withother data from other sources to provide additional information (e.g.,context) for the data.

In some embodiments, the file type of the file may be detected based onoperation of one or more detectors. A given detector may be configuredto detect one or more given file types. For example, a file may bepassed to a first detector configured to detect a first file type. Ifthe file type does not match the first file type, the file may be passedto a second detector configured to detect a second file type. Thepassing of the file between detectors may continue until the file typehas been detected or no match is found from the detectors. In someembodiments, the file may be serially passed among the detectors. Insome embodiments, the file may be passed to multiple detectors inparallel. In some embodiments, a detector may generate metadata for usein transforming the file.

In some embodiments, the file type of the file may be detected based onan arrangement of information within the file. For example, the filetype may be detected based on a structure and/or pattern of informationin the file or based on the actual information included in the file.

In some embodiments, the file may be transformed based on operation ofone or more transformers. A transformer may be associated with adetector. For example, based on a file type being detected by a givendetector, the file may be transformed by a given transformer associatedwith the given detector.

In some embodiments, the computing system may change the pipeline ofoperations and remove one or more effects resulting from previouslyapplied pipeline of operations. Removing the effect(s) of prioroperation pipelines may allow the computing system to resolve thepipeline against known states by atomically handling the pipeline.

In some embodiments, the transformation of the file may be casedependent, upload dependent, or user dependent. For example, one or moredetectors/transformers may be associated with a given case to which thefile belongs/is associated, a given upload in which the file isreceived, or a given user/entity that provided the file.

These and other features of the systems, methods, and non-transitorycomputer readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of various embodiments of the present technology areset forth with particularity in the appended claims. A betterunderstanding of the features and advantages of the technology will beobtained by reference to the following detailed description that setsforth illustrative embodiments, in which the principles of the inventionare utilized, and the accompanying drawings of which:

FIG. 1 illustrates an example environment for providing customizablepipelines for integrating data, in accordance with various embodiments.

FIG. 2 illustrates an example operation flow for integrating data, inaccordance with various embodiments.

FIG. 3 illustrates example pipelines of operations for integrating data,in accordance with various embodiments.

FIG. 4 illustrates a flowchart of an example method, in accordance withvarious embodiments.

FIG. 5 illustrates a block diagram of an example computer system inwhich any of the embodiments described herein may be implemented.

DETAILED DESCRIPTION

A claimed solution rooted in computer technology overcomes problemsspecifically arising in the realm of computer technology. In variousimplementations, a computing system may determine a file to be ingestedinto a data analysis platform (e.g., based on user upload, systemdownload). The file type of the file may be detected. In someembodiments, the file type of the file may be detected based on anarrangement of information within the file. For example, the file typemay be detected based on a structure and/or pattern of information inthe file or based on the actual information included in the file.

The file type of the file may be detected based on operation of one ormore detectors. A given detector may be configured to detect one or moregiven file types. For example, a file may be passed to a first detectorconfigured to detect a first file type. If the file type does not matchthe first file type, the file may be passed to a second detectorconfigured to detect a second file type. The passing of the file betweendetectors may continue until the file type has been detected or no matchis found from the detectors. For example, the file may be seriallypassed among the detectors. As another example, the file may be passedto multiple detectors in parallel. A detector may generate metadata foruse in transforming the file.

The file may be transformed based on the file type. The transformationmay include applying a set of operations to the file. The set ofoperations may correspond to a pipeline of operations associated withthe file type. The transformed file may be stored based on completion ofall operations within the pipeline of operations. The file may betransformed based on operation of one or more transformers. Atransformer may be associated with a detector. For example, based on afile type being detected by a given detector, the file may betransformed by a given transformer associated with the given detector.

The transformation of the file may be case dependent, upload dependent,or user dependent. For example, one or more detectors/transformers maybe associated with a given case to which the file belongs/is associated,a given upload in which the file is received, or a given user/entitythat provided the file.

The pipeline of operations may be defined by a template specification.The pipeline of operations may be customizable. The pipeline ofoperations may include one or more serial operations, paralleloperations, and/or one or more join operations. The join operation(s)may merge two or more results of the parallel operations. For example,data included in a single or multiple files may be operated on inparallel, and the results of the parallel operation may be joinedtogether for use by a data analysis platform. The pipeline of operationsmay include a normalization operation. For example, data included inmultiple files may be of different type (e.g., entered using separatescales) and the data may be normalized (e.g., into a common scale) sothat the data may be joined/used together by a data analysis platform.The pipeline of operations may include an enriching operation. Forexample, data included in a file may be supplemented with other datafrom other sources to provide additional information (e.g., context) forthe data.

The computing system may change the pipeline of operations and removeone or more effects resulting from a previously applied pipeline ofoperations. Removing the effect(s) of a prior pipeline of operations mayallow the computing system to resolve the pipeline against known statesby atomically handling the pipeline.

FIG. 1 illustrates an example environment 100 for providing customizablepipelines for integrating data, in accordance with various embodiments.The example environment 100 may include a computing system 102. Thecomputing system 102 may include one or more processors and memory(e.g., permanent memory, temporary memory). The processor(s) may beconfigured to perform various operations by interpretingmachine-readable instructions stored in the memory. As shown in FIG. 1,in various embodiments, the computing system 102 may include a fileengine 112, a file type engine 114, and a transformation engine 116. Theenvironment 100 may also include one or more datastores that isaccessible to the computing system 102 (e.g., via one or morenetwork(s)). In some embodiments, the datastore(s) may include variousdatabases, application functionalities, application/data packages,and/or other data that are available for download, installation, and/orexecution. While the computing system 102 is shown in FIG. 1 as a singleentity, this is merely for ease of reference and is not meant to belimiting. One or more components/functionalities of the computing system102 described herein may be implemented in a single computing device ormultiple computing devices.

In various embodiments, the file engine 112 may be configured todetermine a file to be ingested into a data analysis platform. A dataanalysis platform may refer to hardware/software components configuredto provide analysis capabilities for data (e.g., database analysistool). A data analysis platform may require data to be stored using aparticular structure/format and/or include one or more particular typesof information. The file to be ingested into the data analysis platformmay be determined (e.g., identified) by the file engine 112 based onreception/identification of the file by the computing system 102 and/orreception/identification of the file by a network/server/computingdevice monitored by the computing system 102.

For example, a user may upload a given file to the computing system 102or a network/server/computing device monitored by the computing system102 via an interface that receives inputs for the data analysisplatform. The file engine 112 may determine that the given file is to beingested into the data analysis platform based on the user's use of theinterface to upload the given file. As another example, a user may usethe computing system 102 or a network/server/computing device monitoredby the computing system 102 to select a particular file for ingestioninto the data analysis platform. The file engine 112 may determine thatthe particular file is to be ingested into the data analysis platformbased on the user's selection of the particular file.

In some embodiments, one or more characteristics/properties of the filerelating to its ingestion into a data analysis platform may be definedwhen the user uploads/selects the file for ingestion. Such informationmay be used to (1) determine that the file is to be ingested into a dataanalysis platform, (2) identify the data analysis platform to which thefile is to be ingested, (3) detect a file type of the file, and/or (4)determine any transformation(s) to be applied to the file. For example,such information may identify/relate to a name of the file, file type ofthe file, destination of the file, related files/case, source of thefile, persons/organizations related to the file, content of the file,context of the file, and/or other information related to the file.

In some embodiments, the file to be ingested into a data analysisplatform may include an archive file. An archive file may refer to afile that includes a collection multiple files in a single file (e.g.,zip file, rar file, 7z file, tar file, jar file, war file). An archivefile may provide for concatenation of files, compression of files,encryption of files, error detection of files, self-extraction of files,and/or other operations relating to archiving of files. An archive filemay include information (e.g., metadata) relating to the files withinthe archive file and/or how the files within the archive file may beextracted.

In some embodiments, the file engine 112 may be configured to determinethat the file to be ingested into a data analysis platform is an archivefile. The file engine 112 may determine that the file is an archive filebased on file format (e.g., file extension), analysis of the file (e.g.,analysis of structure, pattern within the file), user input (e.g., userindication that the file is an archive file during file upload/selectionfor ingestion), information (e.g., metadata) relating to the file,and/or other information. Based on the file to be ingested into a dataanalysis platform being an archive file, the file engine 112 may extractthe files within the archive file.

In various embodiments, the file type engine 114 may be configured todetect a file type of the file to be ingested into the data analysisplatform. The file type engine 114 may detect the file type of the filebased on file format (e.g., file extension), analysis of the file (e.g.,analysis of structure, pattern within the file), user input (e.g., userindication that the file is a particular type of a file), information(e.g., metadata) relating to the file, and/or other information. Forexample, the file type of the file may be detected based on theparticular arrangement of information within the file. That is, the filetype may be detected based on the structure(s) and/or the pattern(s) ofinformation in the file, and/or based on the actual information includedin the file. For example, a file type may be determined to be a portabledocument format (PDF) based on the file extension. The file type may bedetermined to be a specific type of PDF file based on the file extensionand the arrangement of information within the file. As another example,a file type may be determined to be a particular picture format based onthe arrangement of information (e.g., information defining pictures,headers/metadata for the pictures) within the file. As another example,a file type may be determined to be an email of a particular type (e.g.,accessible by particular email client(s)) based on the file extensionand/or the information within the file. As another example, a file typemay be determined to be a document relating to a particular type ofinvestigation (e.g., claims demand information) based on organization ofinformation within the file, actual information within the file (e.g.,case identifier). Other detections of file types are contemplated.

In some embodiments, the file type of the file to be ingested into thedata analysis platform may be detected based on operation of one or moredetectors. A detector may refer to a service, a process, a plugin, anexecutable, and/or other software components. Individuals detectors maybe configured to detect one or more individual file types. For example,a given detector may be configured to detect a given file type. The filetype of the file to be ingested into the data analysis platform may bedetected by passing the file to different detectors. In someembodiments, the file may be serially passed among different detectors.For example, a file may be passed to a first detector configured todetect a first file type. If the file type does not match the first filetype, the file may be passed to a second detector configured to detect asecond file type. The passing of the file between detectors may continueuntil the file type has been detected or no match is found from thedetectors. In some embodiments, the file may be passed to multipledetectors in parallel. For example, a file may be passed on to a firstdetector configured to detect a first file type and a second detectorconfigured to detect a second file type in parallel.

If the file type of the file is not detectable by any detectors, e.g.,the file type may not match any of the file types detectable by thedetectors, then the file may be designated as a non-integratable file.For example, the file may be marked as a generic file which is notconfigured for ingestion into the data analysis platform. In someembodiments, users may update/modify the set of detectors to includeadditional detectors for such generic files.

Once the file type of the file has been detected, the detector (thatdetected the file type) may pass the file to one or more softwarecomponents (e.g., transformers) for transformation. In some embodiments,a detector may generate information for use in transforming the file.For example, the detector that detected the file type may generatemetadata including information relating to the file type and/orinformation relating to transformation(s) to be performed based on thefile type. The information generated by the detector may be passed toone or more software components performing the transformation and/or maybe used to identify the software component(s) to perform thetransformation. In some embodiments, a detector may provide thefile/contents of the file to a data transformation platform fortransformation. For example, the file/contents of the file may bestreamed to a data transformation platform as one or moreobjects/stores.

In various embodiments, the transformation engine 116 may be configuredto transform the file to be ingested into the data analysis platformbased on the file type. Transformation of the file may include applyinga set of operations to the file. An operation may include one or moredata transformations. Such data transformations can include dataextraction, data processing, data integration (e.g., with other data),and data manipulation (e.g., language/mathematical operations, dedupingoperations), to provide some examples. In some embodiments, thetransformation engine 116 may store results of applying the set ofoperations to the file/contents of the file (e.g., the transformedfile/contents) based on completion of all operations within the pipelineof operations. Such storage of the transformed file/contents may ensuredata consistency.

The set of operations may correspond to a pipeline of operationsassociated with the file type. The pipeline of operations may be definedby one or more template specifications. The pipeline of operations mayidentify the operations and the order in which the operations are to beperformed. The pipeline of operations may identify other data to be usedin one or more of the operations (e.g., data to be integrated with/intothe file/contents of the file). The pipeline of operations may enabledynamic selection of options for one or more operations. For example,the pipeline of operations may include one or more operations thatreceive inputs from users (e.g., selection of operation options) forperforming the operation(s).

In some embodiments, the pipeline of operations may include one or moreserial operations, parallel operations, one or more join operations,and/or other operations. Serial operations may refer to operations thatare performed in a sequence. For example, a pipeline of operations mayinclude a first operation and a second operation to be performed on thefile/contents of the file. After the first operation is performed, thesecond operation may be performed. Parallel operations may refer tooperations that are performed simultaneously/overlapping in time. Forexample, referring to the foregoing example of first and secondoperations, the first operation and the second operation may beperformed at the same time on the file/contents of the file. Joinoperations may refer to operations that merge two or more results ofparallel operations. For example, referring to the foregoing example offirst and second parallel operations, the results of the first andsecond parallel operations on the file/contents of the file may bejoined/merged together (e.g., for use by a data analysis platform, forfurther processing).

In some embodiments, the pipeline of operations may include one or morenormalization operations. A normalization operation may refer to anoperation that adjusts the values of some data (e.g., first dataset) sothat it can be used with other data (e.g., second dataset). For example,a first dataset and a second dataset transformed by the transformationengine 116 may be recorded/entered using different scales (e.g.,different measurement standards) or different properties (e.g., thefirst dataset including types of information not included in the seconddataset). The first dataset and the second dataset may be includedwithin a single file or multiple files to be ingested into a dataanalysis platform. The normalization operation can modify the firstand/or the second datasets so that they can be used (e.g., joined,compared, operated) together by the data analysis platform. For example,the normalization operation may adjust the values of the first and/orthe second dataset so that they are recorded using the same scale (e.g.,same measurement standard) and/or may modify the information containedwithin the first and/or the second datasets so that they contain thesame type of information.

In some embodiments, the pipeline of operations may include one or moreenriching operations. An enriching operation may refer to an operationthat enhances the information contained within the file. For example,data included in a file may be supplemented with data from other datasources to provide additional information (e.g., context) for the data.

The pipeline of operations may enable the use of multiple jobs toprocess a file/contents of a file for ingestion into a data analysisplatform. For example, different parts of preparing the file/contents ofthe file for ingestion into the data analysis platform may be separatedin different jobs. The jobs may be modular so that they are transferableto different pipelines and/or modifiable within a pipeline. The jobs mayenable different operations on different portions of the file/differentcontents of the file.

The pipeline of operations may enable the use of linear or branchingpipelines of operations. A linear pipeline may refer to a pipelineincluding serial operations. A branching pipeline may refer to apipeline including parallel/join operations. For example, a branchingpipeline may include a pipeline that starts with one input and ends withone output along with parallel and join operations between the input andthe output. A branching pipeline may include a pipeline that starts withone input and ends with multiple outputs along with parallel operationsbetween the input and the outputs. A branching pipeline may include apipeline that starts with multiple inputs and ends with an output alongwith join operations between the inputs and the output. A branchingpipeline may include a pipeline that starts with multiple inputs andends with multiple outputs along with parallel operations between theinputs and the outputs. Use of different combinations of serial,parallel, join, and/or other operations for pipelines of operations arecontemplated.

Transformation of the file/contents of the file may be performed by adata transformation platform. A data transformation platform may referto hardware/software components configured to provide transformationcapabilities for data (e.g., a data manipulation tool). A datatransformation platform may provide for data extraction, dataprocessing, data integration (e.g., with other data), data manipulation,and/or other data transformations. For example, the file/contents of thefile may be mapped, converted, changed, merged, aggregated, enriched,summarized, filtered, and/or otherwise transformed by the datatransformation platform. A data transformation platform may ensure thatdata from one application/database/platform is usable by otherapplication(s)/database(s)/platform(s). A data transformation platformmay be able to transform the file/contents of the file so that thetransformed file/contents (transformed to a particular structure/formatand/or transformed to include one or more particular types ofinformation) are available for use by a data analysis platform.

Using a data transformation platform for transformation of afile/contents of a file may enable the transformation engine 116 toexecute the pipeline of operations without having codes for individualoperations of the pipeline. Such codes may refer to codes of the datatransformation platform which, when executed, provides data extraction,data processing, data integration, data manipulation, and/or other datatransformations for individual operations of the pipeline. Using thedata transformation platform may reduce the size and/or the complexityof the transformation engine 116 and/or one or more software components(e.g., transformers) used for transformation. For example, rather thancoding the entirety of the operations within a transformer, thetransformer may include codes to request one or more of the operationsto be performed by the data transformation platform.

For example, to perform a join operation, the transformation engine 116may not need to have the raw codes that perform the join operation.Instead, the transformation engine 116 may request the join operation beperformed by the data transformation platform (via applicationprogramming interface(s)). By taking advantage of the capability of thedata transform platform, the transformation engine 116 may be able toexecute the pipeline of operations by offloading the performance of theoperations to the data transform platform. The use of the data transformplatform may increase the type of operations that may be included withinthe pipeline of operations. The pipeline of operations may include anyoperations enabled by the data transform platform. The use of the datatransform platform may enable the transformation engine 116 to use theresources (e.g., hardware/software capabilities) of the data transformplatform for performing operations on the file/contents of the file.

In some embodiments, the file may be transformed based on operation ofone or more transformers. A transformer may refer to a service, aprocess, a plugin, an executable, and/or other software components. Insome embodiments, individual transformers may be associated withindividual detectors. In some embodiments, a single transformer may beassociated with multiple detectors. In some embodiments, multipletransformers may be associated with a single detector. A file/contentsof the file may be transformed by a given transformer based on the filetype of the file being detected by a given detector. For example, basedon a file type being detected by a given detector, the file may betransformed by a given transformer associated with the given detector.For example, based on a file type of a file being detected by an emaildetector, an email transformer may transform the file/contents of thefile. The email transformer may provide integration of an email filetype for use by a data analysis platform. In some embodiments, atransformer may be associated with a given pipeline of operations/agiven template specification of the given pipeline. The operation of thetransformer may include execution of the associated pipeline ofoperations to transform the file/contents of the file.

In some embodiments, one or more transformers may be activated based onuser selection of the transformer(s). For example, a user may beprovided with a view of the transformers identified (transformerscorresponding to the detector that detected the file type) fortransformation of files/contents of files. The user may select one ormore of the identified transformers to activate the transformer andexecute the pipeline of operations corresponding to the selectedtransformers.

In some embodiments, the transformation of the file may be casedependent, upload dependent, and/or user dependent. For example, one ormore detectors/transformers may be associated with a given case to whichthe file belongs/is associated, a given upload in which the file isreceived, or a given user/entity that provided the file. Use of casedependent detectors/transformer allows the same/particulardetectors/transformers to be used for files associated with a givencase. Use of upload dependent detectors/transformer allows thesame/particular detectors/transformers to be used for files uploadedtogether. Use of user-dependent detectors/transforms allows thesame/particular detectors/transformers to be used for files provided bythe same user/entity.

For example, transaction records of two different entities (firstentity, second entity) may be stored using the same file extension(e.g., workbook file format). However, the transaction records of thedifferent entities may be stored differently (e.g., include differentinformation, include information stored using different organization)such that the transformation of the records for ingestion into a dataanalysis platform may require different operations/pipelines ofoperations. For example, the transaction records of the first entity mayrequire different clean-up operations (e.g., filtering, deduping) thanthe transaction records of the second entity.

As another example, a case may include files of different types. Thedifferent types of files may require different operations/pipeline ofoperations for ingestion into a data analysis platform. The files may beuploaded separately, with the upload-specific detectors/transformersbeing used for separate uploads to transform the files/contents of thefiles so that they may be used together. The transformed files/contentsof the files may be used together (e.g., merged, compared) usingcase-specific detectors/transformers. Other uses of case-dependent,upload-dependent, user-dependent detectors/transformers arecontemplated.

The pipeline of operations may be customizable. Customizing a pipelineof operations may include one or more of adding a new operation to thepipeline, removing an operation from the pipeline, and/or modifying anoperation within the pipeline. Customizing pipeline of operations mayinclude changing one or more parameters of one or more operations withinthe pipeline. The pipeline of operations may be customized via changesto the corresponding template specifications.

In some embodiments, a pipeline of operations may be managed to resolvethe pipeline of operations against known states. Managing the pipelinesmay include atomically handling the pipelines and removing effects ofprior versions of pipelines. For example, changing a pipeline ofoperations may include removing one or more effects resulting from aprevious version of the pipeline (e.g., removing effect(s) of previouslyapplied operations). For example, a pipeline of operations may includeoperation A, followed by operation B, followed by operation C. OperationB may have an effect of storing certain information in a database. Thepipeline of operations may have been used to process a file uploaded ata given time (upload A). The pipeline of operations may be modified toremove operation B from the pipeline. If upload A isunprocessed/reprocessed, a special/modified pipeline (e.g., deletepipeline) may be constructed to remove the effects of operation B (thecertain information stored in the database) from the pipeline. In someembodiments, an effect of an operation may be removed by providing anull input into operation B. For example, operation B may have taken agiven input to stored certain information in a database. Passing a nullvalue to operation B may result in removal of the certain informationfrom the database.

FIG. 2 illustrates an example operation flow 200 for integrating data,in accordance with various embodiments. The operation flow 200 may beginwith determination of a file 202 to be ingested into a data analysisplatform. The determination of the file 202 may be performed asdescribed above with respect to the file engine 112. The file 202 maythen be passed to a detection service 204. A service may include one ormore services, processes, plugins, executables, and/or softwarecomponents. The detection service 204 may determine a file type of thefile 202. The determination of the file type of the file may beperformed as described above with respect to the file type engine 114.The file type of the file 202 may then be passed to a transformationselection service 206. The transformation selection service 206 mayidentify one or more transformations that may be performed on the file202 based on the file type of the file 202. The transformation(s) may beidentified as described above with respect the file type engine 114and/or the transformation engine 116 (e.g., based ondetector-transformer pairings). A transformation may include a pipelineof operations. The file 202 may then be passed to a pipeline manager208. The pipeline manager 208 may manage the pipeline of operations ofthe transformation(s) of the file 202. Managing the pipeline ofoperations may include executing the pipeline or operations, dynamicallybuilding/modifying the pipeline of operations, and/or performing otheroperations relating to the pipeline of operations. The pipeline manager208 may provide one or more interfaces (e.g., user interfaces, APIs)that allow users to select, build, and/or modify particulartransformations/pipeline of operations.

FIG. 3 illustrates example pipelines of operations 300, 320, 340, 360for integrating data, in accordance with various embodiments. Othertypes/structures of pipelines of operations are contemplated. Individualoperations within the pipelines of operations 300, 320, 340, 360 mayinclude one or more operations based on type of the file, context of thefile/accessed portion(s) of the file, source of the file/accessedportion(s) of the file, and/or other properties relating to thefile/accessed portion(s) of the file. Individual operations within thepipelines of operations 300, 320, 340, 360 may process/modify thefile/portion(s) of the file.

The pipeline of operations 300 may begin with an operation 302 thataccesses one or more portions of a file. Serial operations 304, 306 maythen be performed on the portion(s) of the file. Parallel to the serialoperations 304, 306, a parallel operation 308 may be performed on theportion(s) of the file. The results of the serial operations 304, 306and the parallel operation 308 may be joined by a join operation 310.The joined results may be provided to a data analysis platform.

The pipeline of operations 320 may begin with an operation 322 thataccesses one or more portions of a file. An operation 324 may then beperformed on the portion(s) of the file. The operation 324 may befollowed by three branching/parallel operations 326, 328, 330. That is,the result of applying the operation 324 on the file may be used toperform three separate operations 326, 328, 330 in parallel. The resultsof the parallel operations 326, 328, 330 may be provided to a dataanalysis platform.

The pipeline of operations 340 may begin with operations 342, 348 thataccess one or more portions of one or more files. For example, theoperation 342 may access one portion of a file and the operation 348 mayaccess another portion of the file. As another example, the operation342 may access portion(s) of a file and the operation 348 may accessportion(s) of another file. The operation 342 may be followed by twobranching/parallel operations 344, 346. The results of the operation 346and the portion(s) of the file accessed by the operation 348 may bejoined by a join operation 350. The results of the operation 344 and thejoin operation 350 may then be joined by a join operation 352. Thejoined results may be provided to a data analysis platform.

The pipeline of operations 360 may begin with operations 362, 370 thataccess one or more portions of one or more files. For example, theoperation 362 may access one portion of a file and the operation 370 mayaccess another portion of the file. As another example, the operation362 may access portion(s) of a file and the operation 370 may accessportion(s) of another file. The operation 362 may be followed by twobranching/parallel operations 364, 368. The operation 364 may befollowed by a serial operation 366. The operation 370 may be followed bya serial operation 372. The results of the operation 368 and theoperation 372 may be joined by a join operation 374. The results of theoperations 366, 374 may be provided to a data analysis platform.

FIG. 4 illustrates a flowchart of an example method 400, according tovarious embodiments of the present disclosure. The method 400 may beimplemented in various environments including, for example, theenvironment 100 of FIG. 1. The operations of method 400 presented beloware intended to be illustrative. Depending on the implementation, theexample method 400 may include additional, fewer, or alternative stepsperformed in various orders or in parallel. The example method 400 maybe implemented in various computing systems or devices including one ormore processors.

At block 402, a file to be ingested into a data analysis platform may bedetermined. At block 404, a file type of the file may be detected. Atblock 406, the file may be transformed based on the file type. Thetransformation may include applying a set of operations to the file. Theset of operations may correspond to a pipeline of operations associatedwith the file type. At block 408, the pipeline of operations mayoptionally be changed. At block 410, one or more effects of a priorpipeline of operations may optionally be removed.

Hardware Implementation

The techniques described herein are implemented by one or morespecial-purpose computing devices. The special-purpose computing devicesmay be hard-wired to perform the techniques, or may include circuitry ordigital electronic devices such as one or more application-specificintegrated circuits (ASICs) or field programmable gate arrays (FPGAs)that are persistently programmed to perform the techniques, or mayinclude one or more hardware processors programmed to perform thetechniques pursuant to program instructions in firmware, memory, otherstorage, or a combination. Such special-purpose computing devices mayalso combine custom hard-wired logic, ASICs, or FPGAs with customprogramming to accomplish the techniques. The special-purpose computingdevices may be desktop computer systems, server computer systems,portable computer systems, handheld devices, networking devices or anyother device or combination of devices that incorporate hard-wiredand/or program logic to implement the techniques.

Computing device(s) are generally controlled and coordinated byoperating system software, such as iOS, Android, Chrome OS, Windows XP,Windows Vista, Windows 7, Windows 8, Windows Server, Windows CE, Unix,Linux, SunOS, Solaris, iOS, Blackberry OS, VxWorks, or other compatibleoperating systems. In other embodiments, the computing device may becontrolled by a proprietary operating system. Conventional operatingsystems control and schedule computer processes for execution, performmemory management, provide file system, networking, I/O services, andprovide a user interface functionality, such as a graphical userinterface (“GUI”), among other things.

FIG. 5 is a block diagram that illustrates a computer system 500 uponwhich any of the embodiments described herein may be implemented. Thecomputer system 500 includes a bus 502 or other communication mechanismfor communicating information, one or more hardware processors 504coupled with bus 502 for processing information. Hardware processor(s)504 may be, for example, one or more general purpose microprocessors.

The computer system 500 also includes a main memory 506, such as arandom access memory (RAM), cache and/or other dynamic storage devices,coupled to bus 502 for storing information and instructions to beexecuted by processor 504. Main memory 506 also may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by processor 504. Such instructions, whenstored in storage media accessible to processor 504, render computersystem 500 into a special-purpose machine that is customized to performthe operations specified in the instructions.

The computer system 500 further includes a read only memory (ROM) 508 orother static storage device coupled to bus 502 for storing staticinformation and instructions for processor 504. A storage device 510,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 502 for storing information andinstructions.

The computer system 500 may be coupled via bus 502 to a display 512,such as a cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 514,including alphanumeric and other keys, is coupled to bus 502 forcommunicating information and command selections to processor 504.Another type of user input device is cursor control 516, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 504 and for controllingcursor movement on display 512. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

The computing system 500 may include a user interface module toimplement a GUI that may be stored in a mass storage device asexecutable software codes that are executed by the computing device(s).This and other modules may include, by way of example, components, suchas software components, object-oriented software components, classcomponents and task components, processes, functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuitry, data, databases, data structures, tables, arrays,and variables.

In general, the word “module,” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,possibly having entry and exit points, written in a programminglanguage, such as, for example, Java, C or C++. A software module may becompiled and linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted programming languagesuch as, for example, BASIC, Perl, or Python. It will be appreciatedthat software modules may be callable from other modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules configured for execution on computingdevices may be provided on a computer readable medium, such as a compactdisc, digital video disc, flash drive, magnetic disc, or any othertangible medium, or as a digital download (and may be originally storedin a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules may be comprised of connectedlogic units, such as gates and flip-flops, and/or may be comprised ofprogrammable units, such as programmable gate arrays or processors. Themodules or computing device functionality described herein arepreferably implemented as software modules, but may be represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage.

The computer system 500 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computer systemcauses or programs computer system 500 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputer system 500 in response to processor(s) 504 executing one ormore sequences of one or more instructions contained in main memory 506.Such instructions may be read into main memory 506 from another storagemedium, such as storage device 510. Execution of the sequences ofinstructions contained in main memory 506 causes processor(s) 504 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “non-transitory media,” and similar terms, as used hereinrefers to any media that store data and/or instructions that cause amachine to operate in a specific fashion. Such non-transitory media maycomprise non-volatile media and/or volatile media. Non-volatile mediaincludes, for example, optical or magnetic disks, such as storage device510. Volatile media includes dynamic memory, such as main memory 506.Common forms of non-transitory media include, for example, a floppydisk, a flexible disk, hard disk, solid state drive, magnetic tape, orany other magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with patterns of holes, a RAM, aPROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge, and networked versions of the same.

Non-transitory media is distinct from but may be used in conjunctionwith transmission media. Transmission media participates in transferringinformation between non-transitory media. For example, transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 502. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 504 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 500 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 502. Bus 502 carries the data tomain memory 506, from which processor 504 retrieves and executes theinstructions. The instructions received by main memory 506 mayoptionally be stored on storage device 510 either before or afterexecution by processor 504.

The computer system 500 also includes a communication interface 518coupled to bus 502. Communication interface 518 provides a two-way datacommunication coupling to one or more network links that are connectedto one or more local networks. For example, communication interface 518may be an integrated services digital network (ISDN) card, cable modem,satellite modem, or a modem to provide a data communication connectionto a corresponding type of telephone line. As another example,communication interface 518 may be a local area network (LAN) card toprovide a data communication connection to a compatible LAN (or WANcomponent to communicated with a WAN). Wireless links may also beimplemented. In any such implementation, communication interface 518sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP inturn provides data communication services through the world wide packetdata communication network now commonly referred to as the “Internet”.Local network and Internet both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on network link and throughcommunication interface 518, which carry the digital data to and fromcomputer system 500, are example forms of transmission media.

The computer system 500 can send messages and receive data, includingprogram code, through the network(s), network link and communicationinterface 518. In the Internet example, a server might transmit arequested code for an application program through the Internet, the ISP,the local network and the communication interface 518.

The received code may be executed by processor 504 as it is received,and/or stored in storage device 510, or other non-volatile storage forlater execution.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

The various features and processes described above may be usedindependently of one another, or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure. The foregoing description details certainembodiments of the invention. It will be appreciated, however, that nomatter how detailed the foregoing appears in text, the invention can bepracticed in many ways. As is also stated above, it should be noted thatthe use of particular terminology when describing certain features oraspects of the invention should not be taken to imply that theterminology is being re-defined herein to be restricted to including anyspecific characteristics of the features or aspects of the inventionwith which that terminology is associated. The scope of the inventionshould therefore be construed in accordance with the appended claims andany equivalents thereof.

Engines, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, engines, or mechanisms. Engines may constitute eithersoftware engines (e.g., code embodied on a machine-readable medium) orhardware engines. A “hardware engine” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware engines ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware engine that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware engine may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware engine may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware engine may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware engine may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware enginemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwareengines become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware engine mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware engine” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented engine” refers to a hardware engine. Consideringembodiments in which hardware engines are temporarily configured (e.g.,programmed), each of the hardware engines need not be configured orinstantiated at any one instance in time. For example, where a hardwareengine comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware engines) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware engine at one instance oftime and to constitute a different hardware engine at a differentinstance of time.

Hardware engines can provide information to, and receive informationfrom, other hardware engines. Accordingly, the described hardwareengines may be regarded as being communicatively coupled. Where multiplehardware engines exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware engines. In embodiments inwhich multiple hardware engines are configured or instantiated atdifferent times, communications between such hardware engines may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware engines have access.For example, one hardware engine may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware engine may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware engines may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented engine” refers to ahardware engine implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

It will be appreciated that an “engine,” “system,” “data store,” and/or“database” may comprise software, hardware, firmware, and/or circuitry.In one example, one or more software programs comprising instructionscapable of being executable by a processor may perform one or more ofthe functions of the engines, data stores, databases, or systemsdescribed herein. In another example, circuitry may perform the same orsimilar functions. Alternative embodiments may comprise more, less, orfunctionally equivalent engines, systems, data stores, or databases, andstill be within the scope of present embodiments. For example, thefunctionality of the various systems, engines, data stores, and/ordatabases may be combined or divided differently.

“Open source” software is defined herein to be source code that allowsdistribution as source code as well as compiled form, with awell-publicized and indexed means of obtaining the source, optionallywith a license that allows modifications and derived works.

The data stores described herein may be any suitable structure (e.g., anactive database, a relational database, a self-referential database, atable, a matrix, an array, a flat file, a documented-oriented storagesystem, a non-relational No-SQL system, and the like), and may becloud-based or otherwise.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, engines, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred implementations, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present invention contemplates that, to theextent possible, one or more features of any embodiment can be combinedwith one or more features of any other embodiment.

The invention claimed is:
 1. A system comprising: one or moreprocessors; and a memory storing instructions that, when executed by theone or more processors, cause the system to perform: determining a fileis to be ingested into a data analysis platform in response to a userselecting the file, wherein the file is of a file type; with existingdetectors, detecting the file type based on structure information,pattern information, or actual information within the file; in responseto the file being undetectable by the detectors, marking the file typeas generic and creating a new detector to detect the file type; inresponse to one of the existing detectors or the new detector detectingthe file type: generating, by the one of the existing detectors or thenew detector, metadata including information relating to the file typeor information relating to one or more operations to be performed basedon the file type; identifying, by the one of the existing detectors orthe new detector, a particular transformer, wherein the particulartransformer executes at least a portion of the one or more operationsbased on the metadata; and passing the metadata to the particulartransformer; and removing an effect resulting from a previous version ofa pipeline of operations or from the one or more operations byconstructing a delete pipeline.
 2. The system of claim 1, wherein theone or more operations include one or more serial operations, one ormore parallel operations, and one or more join operations, the one ormore join operations merging two or more results of the paralleloperations.
 3. The system of claim 1, wherein the one or more operationsinclude a normalizing operation.
 4. The system of claim 1, wherein thedelete pipeline deletes previously stored information in a database. 5.The system of claim 1, wherein the detecting comprises continuouslypassing the file to different detectors until one of the detectorsrecognizes the file.
 6. A method implemented by a computing systemincluding one or more processors and storage media storingmachine-readable instructions, wherein the method is performed using theone or more processors, the method comprising: determining a file is tobe ingested into a data analysis platform in response to a userselecting the file, wherein the file is of a file type; with existingdetectors, detecting the file type based on structure information,pattern information, or actual information within the file; in responseto the file being undetectable by the detectors, marking the file typeas generic and creating a new detector to detect the file type; inresponse to one of the existing detectors or the new detector detectingthe file type: generating, by the one of the existing detectors or thenew detector, metadata including information relating to the file typeor information relating to one or more operations to be performed basedon the file type; identifying, by the one of the existing detectors orthe new detector, a particular transformer, wherein the particulartransformer executes at least a portion of the one or more operationsbased on the metadata; and passing the metadata to the particulartransformer; and removing an effect resulting from a previous version ofa pipeline of operations or from the one or more operations byconstructing a delete pipeline.
 7. The method of claim 6, wherein theone or more operations include one or more serial operations, one ormore parallel operations, and one or more join operations, the one ormore join operations merging two or more results of the paralleloperations.
 8. The method of claim 6, wherein the one or more operationsinclude a normalizing operation.
 9. The method of claim 6, wherein thedelete pipeline deletes previously stored information in a database. 10.The method of claim 6, wherein the detecting comprises continuouslypassing the file to different detectors until one of the detectorsrecognizes the file.
 11. A non-transitory computer readable mediumcomprising instructions that, when executed, cause one or moreprocessors to perform: determining a file is to be ingested into a dataanalysis platform in response to a user selecting the file, wherein thefile is of a file type; with existing detectors, detecting the file typebased on structure information, pattern information, or actualinformation within the file; in response to the file being undetectableby the detectors, marking the file type as generic and creating a newdetector to detect the file type; in response to one of the existingdetectors or the new detector detecting the file type: generating, bythe one of the existing detectors or the new detector, metadataincluding information relating to the file type or information relatingto one or more operations to be performed based on the file type;identifying, by the one of the existing detectors or the new detector, aparticular transformer to perform at least a portion of the one or moreoperations based on the metadata; and passing the metadata to theparticular transformer; and removing an effect resulting from a previousversion of a pipeline of operations or from the one or more operationsby constructing a delete pipeline.
 12. The non-transitory computerreadable medium of claim 11, wherein the one or more operations includeone or more serial operations, one or more parallel operations, and oneor more join operations, the one or more join operations merging two ormore results of the parallel operations.
 13. The non-transitory computerreadable medium of claim 11, wherein the one or more operations includea normalizing operation.
 14. The non-transitory computer readable mediumof claim 11, wherein the delete pipeline deletes previously storedinformation in a database.